The problem of determining the hyperplane with minimum number of misclassifications
in a binary classification problem is known to be NP-hard [1]}.
In mainstream machine learning literature this is called minimizing the 01 loss
[2]} given in Objective REF ,
\(\frac{1}{2n}\operatornamewithlimits{arg\,min}_{w,w_0} \sum _i (1-sign(y_i(w^Tx_i+w_0)))\) 
